Maximum Power Estimation Research Articles

Research on active participation of distributed photovoltsics in power grid primary frequency control strategy

Distributed photovoltaic systems can actively contribute to the primary frequency regulation of the power grid by reserving capacity. Traditional power reduction methods often employ fixed load reduction ratios, potentially resulting in inadequate frequency regulation capacity and unnecessary reserve power. This paper centers on optimizing power reserve control, starting with the construction of a two-stage model for a photovoltaic grid-connected inverter power generation system. It includes the design of a maximum power estimation method and the implementation of photovoltaic power reduction operation. The article proposes a strategy to utilize photovoltaic backup capacity for achieving primary frequency modulation effects in a short time scale. Additionally, it adopts a variable power reserve ratio operation strategy over the long term, aiming to enhance photovoltaic power generation and optimize solar energy utilization without compromising the grid’s frequency and quality. Finally, a MATLAB/Simulink model is developed to validate the effectiveness of the control strategy. Simulation results indicate that the proposed strategy satisfies frequency regulation requirements, enhances power generation efficiency, and improves the economic viability of photovoltaic operations.

Stand-alone PV-SOFC-battery power system based on ANFIS controller for electric vehicle charging

Purpose This study aims to offer a hybrid stand-alone system for electric vehicle (EV) charging stations (CS), an emerging power scheme due to the availability of renewable and environment-friendly energy sources. This paper presents the analysis of a photovoltaic (PV) with an adaptive neuro-fuzzy inference system (ANFIS) algorithm, solid oxide fuel cell (SOFC) and a battery storage scheme incorporated for EV CS in a stand-alone mode. In previous studies, either the hydrogen fuel of SOFC or the irradiance is controlled using artificial neural network. These parameters are not controlled simultaneously using an ANFIS-based approach. The ANFIS-based stand-alone hybrid system controlling both the fuel flow of SOFC and the irradiance of PV is discussed in this paper. Design/methodology/approach The ANFIS algorithm provides an efficient estimation of maximum power (MP) to the nonlinear voltage–current characteristics of a PV, integrated with a direct current–direct current (DC–DC) converter to boost output voltage up to 400 V. The issue of fuel starvation in SOFC due to load transients is also mitigated using an ANFIS-based fuel flow regulator, which robustly provides fuel, i.e. hydrogen per necessity. Furthermore, to ensure uninterrupted power to the CS, PV is integrated with a SOFC array, and a battery storage bank is used as a backup in the current scenario. A power management system efficiently shares power among the aforesaid sources. Findings A comprehensive simulation test bed for a stand-alone power system (PV cells and SOFC) is developed in MATLAB/Simulink. The adaptability and robustness of the proposed control paradigm are investigated through simulation results in a stand-alone hybrid power system test bed. Originality/value The simulation results confirm the effectiveness of the ANFIS algorithm in a stand-alone hybrid power system scheme.

Indoor environment PV applications: Estimation of the maximum harvestable power

The use of PV modules for powering sensors in an indoor environment requires that, during the design process, the harvestable power be evaluated and compared with the power requirements of the load device to validate their compatibility for a given type of light source and level of illumination. The models reported recently to relate power to illumination during power estimation are seen to result in large estimation errors in the case of an indoor environment. This paper seeks to propose an appropriate model that can be exploited during maximum power estimation in an indoor environment to relate power to illumination level. Proceeding from fitting measured data to the three-diode model of the PV module and based on the errors and execution time, the Grey Wolf Optimisation Algorithm, among others, proved to be accurate for estimating maximum power. The relationship between maximum power and illumination was seen to take the form of a second-degree polynomial. This was validated by comparing the values of power estimated by this model to the measured values as well as values estimated with the linear model, the exponential model, and the model proposed by Joseph Amajama. The proposed model proved to be more accurate than the models reported, recording errors of not more than 5 %, which is acceptable for system design. This will go a long way towards facilitating the design of indoor energy harvesting systems that can be used for powering smart sensors and other IoT devices.

Physics-Based Model Predictive Control for Power Capability Estimation of Lithium-Ion Batteries

The power capability of a lithium-ion battery signifies its capacity to continuously supply or absorb energy within a given time period. For an electrified vehicle, knowing this information is critical to determining control strategies such as acceleration, power split, and regenerative braking. Unfortunately, such an indicator cannot be directly measured and is usually challenging to be inferred for today's high-energy type of batteries with thicker electrodes. In this work, we propose a novel physics-based battery power capability estimation method to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power, but also the internal constraints on lithium plating and thermal runaway, can be readily taken into account. The online estimation of maximum power is accomplished by formulating and solving a constrained nonlinear optimization problem. Due to the relatively high system order, high model nonlinearity, and long prediction horizon, a scheme based on multistep nonlinear model predictive control is found to be computationally affordable and accurate.

An Artificial Neural Network for Solar Energy Prediction and Control Using Jaya-SMC

In recent years, researchers have focused on improving the efficiency of photovoltaic systems, as they have an extremely low efficiency compared to fossil fuels. An obvious issue associated with photovoltaic systems (PVS) is the interruption of power generation caused by changes in solar radiation and temperature. As a means of improving the energy efficiency performance of such a system, it is necessary to predict the meteorological conditions that affect PV modules. As part of the proposed research, artificial neural networks (ANNs) will be used for the purpose of predicting the PV system’s current and voltage by predicting the PV system’s operating temperature and radiation, as well as using JAYA-SMC hybrid control in the search for the MPP and duty cycle single-ended primary-inductor converter (SEPIC) that supplies a DC motor. Data sets of size 60538 were used to predict temperature and solar radiation. The data set had been collected from the Department of Systems Engineering and Automation at the Vitoria School of Engineering of the University of the Basque Country. Analyses and numerical simulations showed that the technique was highly effective. In combination with JAYA-SMC hybrid control, the proposed method enabled an accurate estimation of maximum power and robustness with reasonable generality and accuracy (regression (R) = 0.971, mean squared error (MSE) = 0.003). Consequently, this study provides support for energy monitoring and control.

Sensorless Optimal Control Strategy of PMSG in Wind Energy Conversion System

This paper presents a sensorless optimal control strategy of Permanent Magnet Synchronous Generator (PMSG) used in Wind Energy Conversion System (WECS). An approach to develop a simple reduced speed observer for PMSG is developed. The speed and rotor position estimation of the PMSG are obtained by only measuring phase voltage and current. Maximum wind energy extraction is achieved by running the wind turbine generator in variable-speed mode. The rotor speed is allowed to vary in sympathy with the wind speed by maintaining the tip speed ratio to the value that maximizes aerodynamic efficiency. Advantages of this mechanical sensorless control strategy for maximum power estimation are demonstrated by digital simulation of the WECS.

Maximum Power Estimation of Total Cross-Tied Connected PV Cells in Different Shading Conditions for High Current Application

In the existing solar photovoltaic (PV) modules, cells are connected in series. The performance of the series-connected module degrades severely during partial shading conditions (PSCs). Furthermore, the use of bypass diode causes multiple peak (local and global peaks) problems. Therefore, a total cross tied (TCT) connection has been proposed in this article to connect the cells in a module (TCT connected module) that gives a better performance during partial shading without multiple peak problems. A novel analytical algorithm is presented in this article, which will help the PV installers to estimate the output power of a TCT connected module without using any sophisticated measuring instruments except irradiance and temperature measurement. The proposed analytical algorithm is simple, robust, and user-friendly, which can be widely used in multidisciplinary research. The output of the proposed model is verified with the simulation and laboratory-scale experimental results, which shows less than 4.58% error. Moreover, the proposed algorithm is implemented in a 10 MW solar plant, which shows around 174.44% performance improvement (PI) compared to the existing TCT connected array. Furthermore, the current value of the TCT connected module is very high, which can be effectively used in high current applications such as welding even in PSC.

Electricity Demand Forecasting of Hospital Buildings in Istanbul

Electricity demand forecasting is essential for utilities. For the consumer, predictability of demand is vital for efficient operation, installation, sizing and maintenance planning. Hospitals, which are among the institutions with high-energy consumption, provide uninterrupted service 24 h a day, 7 days a week. Every hospital building is unique, and many do not conform to a typical shape or floor plan. Depending on the services provided, each hospital can differ significantly in terms of energy demand. Therefore, demand forecasting is one of the most complex elements of hospital construction. Although there are many studies on energy optimization related to hospital buildings in the literature, there is a knowledge gap regarding the maximum power estimation of hospitals. In this study, the annual electrical energy use of 23 public hospitals with over 100 beds in Istanbul is measured, and after determining the monthly peak loads, two new forecasting models are generated using regression techniques for maximum demand forecasting. It is determined that the design criteria used in power calculations in hospitals was very high. A positive result was obtained from the linear regression technique, which is one of the basic regression techniques, and it was shown that the maximum power needs of the hospital can be estimated with great confidence by determining a new design factor in the light of the determined values. This study allows designers to set maximum demands and select transformer and generator sizes with a single formula.

DC-side synchronous active power control of two-stage photovoltaic generation for frequency support in Islanded microgrids

Virtual synchronous generator control (VSG) is an attractive method for the grid-tied inverter to provide inertia and frequency support. However, it brings some troubles on DC-link voltage control when it is applied to two-stage photovoltaic (PV) power generation. This study proposes a DC-Side synchronous active power Control for two-stage photovoltaic (PV) power generation without energy storage. Synchronous active power Control is applied to DC–DC converters, while the grid-tied inverter still maintains the DC-link voltage constant. The DC–DC converters flexibly regulate active power to provide the simultaneous inertial and primary frequency response like a synchronous generator. A simple sensorless iterative maximum power estimation method is introduced and integrated into the DC-side VSG strategy. It can estimate a rough MAP based on one sample point and converge to an accurate value along with the VSG control. The effectiveness and advantages of the control strategy are validated through case studies on an islanded microgrid that included PV generations with a DC-bus collection system. The results show that the proposed strategy significantly improves the frequency nadir and the steady frequency and maintains DC-link voltage stability during irradiance change conditions.

Gaussian Kernel Based SVR Model for Short-Term Photovoltaic MPP Power Prediction

Predicting the power obtained at the output of the photovoltaic (PV) system is fundamental for the optimum use of the PV system. However, it varies at different times of the day depending on intermittent and nonlinear environmental conditions including solar irradiation, temperature and the wind speed, Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve. In this study, a Gaussian kernel based Support Vector Regression (SVR) prediction model using multiple input variables is proposed for estimating the maximum power obtained from using perturb observation method in the different irradiation and the different temperatures for a short-term in the DC-DC boost converter at the PV system. The performance of the kernel-based prediction model depends on the availability of a suitable kernel function that matches the learning objective, since an unsuitable kernel function or hyper parameter tuning results in significantly poor performance. In this study for the first time in the literature both maximum power is obtained at maximum power point and short-term maximum power estimation is made. While evaluating the performance of the suggested model, the PV power data simulated at variable irradiations and variable temperatures for one day in the PV system simulated in MATLAB were used. The maximum power obtained from the simulated system at maximum irradiance was 852.6 W. The accuracy and the performance evaluation of suggested forecasting model were identified utilizing the computing error statistics such as root mean square error (RMSE) and mean square error (MSE) values. MSE and RMSE rates which obtained were 4.5566 * 10−04 and 0.0213 using ANN model. MSE and RMSE rates which obtained were 13.0000 * 10−04 and 0.0362 using SWD-FFNN model. Using SVR model, 1.1548 * 10−05 MSE and 0.0034 RMSE rates were obtained. In the short-term maximum power prediction, SVR gave higher prediction performance according to ANN and SWD-FFNN.

Adaptive Parameter Identification of a Fuel Cell System for Health-Conscious Energy Management Applications

Since a proton exchange membrane (PEM) fuel cell (FC) has time-varying characteristics, its online characteristics estimation (voltage, power, internal resistance, etc.) is becoming a key step in designing an energy management strategy (EMS) for hybrid FC vehicles. In this respect, this paper proposes a new method based on Lyapunov adaptation law to estimate the linear and nonlinear parameters of a renowned PEMFC model in the literature. Unlike most of similar estimators, the suggested approach determines the maximum current, which is a nonlinear parameter, online while guaranteeing the system closed-loop stability. This parameter is normally assumed to be constant while it changes through time owing to degradation and operating conditions variation. This alteration makes the model imprecise while extracting some important characteristics, such as maximum power and polarization curve. Therefore, it needs to be regularly updated along with other parameters. To demonstrate the capability of the suggested method, a detailed comparison is provided with the well-known extended Kalman filter (EKF) as an attested nonlinear estimator. Moreover, to highlight the effectiveness of the nonlinearity consideration, a comparison with KF is performed where the nonlinear parameter is considered constant. The performed experiments on a 500-W PEMFC show that the proposed method can be over twice as accurate as EKF and KF concerning the estimation of maximum power and current while its runtime is nearly half of them.

Scenario-Based Investigation on the Effect of Partial Shading Condition Patterns for Different Static Solar Photovoltaic Array Configurations

This paper presents an in-depth analysis and investigation on the performance of static photovoltaic (PV) array configurations subjected to various partial shading conditions (PSCs). Under PSCs, the electrical characteristics of the PV modules are critically monitored and reasons for their behavioral changes are highlighted. By doing so, this study aims to improve the efficiency of PV systems by minimizing mismatch losses and determining the optimum array configuration which is characterized by the highest maximum power and lowest relative losses under PSCs. Besides, this study complements and carries forward the previous studies through the detailed analysis of each configuration subjected to various practically probable PSCs. Three different PV array sizes ( $5\times4$ , $5\times5$ , and $3\times10$ ) are used to analyze the results and performance under considered shading scenarios. MATLAB/Simulink platform is used to model and simulate the PV array using the single diode (5-parameters) model. In-depth analysis of current flow across cross-ties and bypass diodes activation shows that the diagonal shading pattern leads to lower power loss (PL). Besides, the Total Cross-Tied (TCT) configuration demonstrates superior performance under most of the PSCs compared to other configurations. These results provide valuable information about the performance of PV array which may lead to better estimation and prediction of global maximum power (GMP) generation of a PV system.

Islanding operation scheme for DC microgrid utilizing pseudo Droop control of photovoltaic system

The penetration levels of renewable energy sources, energy storage systems, and electric vehicles are increasing with the increase in the demand to solve energy and environmental issues. These distributed generators and energy storages can be efficiently managed in a DC smart grid by coordinated control. This paper proposes novel management schemes for DC systems to improve the reliability of power supply and improve the introduction ratio of renewable energy sources (RES). The proposed DC smart grid includes multiple DC microgrids. These configurations and control systems must be designed on the assumption of islanding operation of DC systems. In the islanding power system with the high penetration of RES, not only the utilization of energy storage but also load shedding and RES control is important. Hence, an efficient control strategy by using a storage battery, fuel cell and electrolyzer units, heat pump units, and a photovoltaic (PV) system is presented for stable islanding operation of DC microgrid. As the most important concept, a pseudo Droop control is proposed that utilizes the characteristics of the PV module. The pseudo Droop control enables the PV system to suppress the output power properly by using a simple control system without the maximum power estimation. Fault ride through protection for DC system is also adapted to the DC distribution lines. The performance of the proposed DC smart grid is demonstrated through MATLAB®/Simscape ElectricalTM simulations. The simulation results show that the DC microgrid can maintain stable islanding operation under the proposed management schemes when faults occur in the DC distribution lines.

Parameter Identification and Maximum Power Estimation of Battery/Supercapacitor Hybrid Energy Storage System Based on Cramer–Rao Bound Analysis

This paper presents the analysis, design, and experimental validation of parameter identification of battery/supercapacitor (SC) hybrid energy storage system (HESS) for the purpose of condition monitoring and maximum power estimation. The analytic bounds on the error of battery and SC parameter identification, considering voltage measurement noise, are obtained based on the Fisher information matrix and Cramer–Rao bound analysis. The identification of different parameters requires different signal patterns to ensure high accuracy, rendering tradeoffs in the multiparameter identification process. With an appropriately designed current profile, HESS parameters are identified using recursive least squares with a forgetting factor. The identified parameters are then used to estimate the maximum power capability of the HESS. The maximum power capabilities of the battery and SC are estimated for both 1 and 30 s time horizons. The parameter identification algorithm can be applied to systems including either batteries or SCs when the optimal excitation current can be injected. Experimental validation is conducted on an HESS test-bed, which shows that the proposed algorithm is effective in estimating the HESS maximum power based on appropriate current excitation.

Application of the Maximum Power Point Tracking (MPPT) Algorithm on Lithium-Ion Batteries

This work focuses on the application of the maximum power point tracking (MPPT) algorithm to discharge lithium-ion batteries at their maximum possible power. This algorithm is routinely applied in photovoltaic applications but has only seen limited application in battery applications so far. A simple form of the MPPT algorithm is used in this work. A fixed magnitude of current is applied, and the new value of power is calculated. If the new value of power is greater than the previous one, the current is varied in the same direction until the maximum power is reached. This algorithm is simple to implement and consumes low computing power. However, the magnitude of a current step should be optimized. If the current step is very small, the time taken to reach the maximum power will be too long. And if the current step is very large, there will be a large oscillation near the power maxima. Therefore, we study the effect of current steps during application of the MPPT algorithm on lithium-ion batteries. Comparative studies between various optimization methods such as gradient descent, steepest descent and Newton’s method are made. The effect of discharging batteries at their maximum power on their life cycle is also investigated. The information obtained from this study can be a useful guide to designing a battery management system that gets the maximum performance out of a lithium-ion battery. References Selvan, P. Nair and Umayal, “A review of photovoltaic MPPT algorithms,” International Journal of Electrical and Computer Engineering, 6, 2016, 567-582.A. B. Vieira and A. M. Mota, “Maximum power point tracker applied in batteries charging with PV panels,” 2008 IEEE International Symposium on Industrial Electronics, Cambridge, 2008, 202-207.Kim, S. Mohan, J. B. Siegal and A. G. Stefanopoulou, “Maximum power estimation of lithium-ion batteries accounting for thermal and electrical constraints,” Proceedings of the ASME 2013 Dynamic Systems and Control Conference, California, 2013.

Discharging Batteries Using Maximum Power Point Tracking (MPPT) Algorithm

In this work, we seek to answer the following question: “How long will a battery last if it is discharged at its maximum possible power?” Various primary battery chemistries (Zn-MnO2, Ag-AgO, Zn-air) and secondary battery chemistries (Ni-MH, Li-ion) are investigated. The maximum power point tracking (MPPT) algorithm, which has been used in photovoltaic devices so far, is applied to draw the maximum power possible from these batteries. In the MPPT algorithm used here, a fixed magnitude of current is applied and the new value of power is calculated. If the new value of power is greater than the previous one, the current is varied in the same direction until the maximum power is reached. This algorithm is simple to implement and consumes low computing power. However, the magnitude of a current step should be optimized. If the current step is very small, the time taken to reach the maximum power will be too long. And if the current step is very large, there will be a large oscillation near the power maxima. We present the effect of current steps in reaching the maximum power on batteries. The optimal current step is different from one battery to another. For fair comparison, the current steps are normalized to the discharge rate to achieve 1 C capacity. For rechargeable batteries, the effect of charging rates on the maximum power obtainable is also investigated. The information obtained from this study can be a useful guide to designing a battery management system that gets the maximum performance out of any battery. References Selvan, P. Nair and Umayal, “A review of phot voltaic MPPT algorithms,” International Journal of Electrical and Computer Engineering, 6, 2016, 567-582.A. B. Vieira and A. M. Mota, “Maximum power point tracker applied in batteries charging with PV panels,” 2008 IEEE International Symposium on Industrial Electronics, Cambridge, 2008, 202-207.Kim, S. Mohan, J. B. Siegal and A. G. Stefanopoulou, “Maximum power estimation of lithium-ion batteries accounting for thermal and electrical constraints,” Proceedings of the ASME 2013 Dynamic Systems and Control Conference, California, 2013.

A maximum power point prediction method for group control of photovoltaic water pumping systems based on parameter identification

This paper puts forward a maximum power estimation method based on the photovoltaic array (PVA) model to solve the optimization problems about group control of the PV water pumping systems (PVWPS) at the maximum power point (MPP). This method uses the improved genetic algorithm (GA) for model parameters estimation and identification in view of multi P-V characteristic curves of a PVA model, and then corrects the identification results through least square method. On this basis, the irradiation level and operating temperature under any condition are able to estimate so an accurate PVA model is established and the MPP none-disturbance estimation is achieved. The simulation adopts the proposed GA to determine parameters, and the results verify the accuracy and practicability of the methods.

Assessment of Anaerobic Endurance Based on Selected Biochemical Parameters in 400 m/400 m Hurdles Male Athletes

The ongoing development of the modern society may also be easily noticed in sports. Currently, to be able to compete at the highest level at major sporting events, it is essential to conduct scientific research to estimate the individual potential and skill level of an athlete. The aim of this study was to assess the anaerobic endurance in men competing in the running events of 400 m/400 m hurdles based on the estimation of maximum power, as well as selected biochemical and physiological parameters measured in a cardiac stress test at a supramaximal intensity. Seven athletes participated in the study and were subjected to a modified version of the Wingate test (Bar-Or, Dotan, Inbar, 1997; Bar-Or, Inbar, Skinder, 1996) which involved performing three runs of a 12‑second endurance test on a cycloergometer at short intervals. This modification of the test allowed a more precise observation and a better understanding of the physiological and biochemical changes that decrease the efficiency as a result of repeated physical effort, which may significantly affect the assessment of the preparation of an athlete, and thus the assessment of the training process. The most significant changes in the course of the conducted study were observed in the values of the following biochemical parameters: glucose levels, acid–base balance of the blood and lactate levels.

Modeling, analysis and comparison of solar photovoltaic array configurations under partial shading conditions

The main purpose of this paper is to model, study and analyze the performance of different photovoltaic (PV) array configurations under various partial shading conditions (PSC). The aim is to improve the efficiency of PV systems in general by reducing mismatch losses and to select the most appropriate PV array configuration which provides the best performance i.e. the highest maximum power and hence the lowest relative power losses under partial shading conditions. For this purpose, a comprehensive study which considers all the available PV array configurations: Series (S), Parallel (P), Series–Parallel (SP), Total-Cross-Tied (TCT), Bridge-Linked (BL), and Honey-Comb (HC), is carried out under all possible scenarios of shading. The performance and output characteristics of these array configurations are analyzed and compared by using a 6×4 PV array size under various possible shading scenarios.The Bishop model of a photovoltaic module which describes best the solar cell behavior at negative voltages is considered in this paper for modeling the PV arrays, and it is implemented by using the Simulink and SimPower software.On the whole, the obtained results prove the superiority of the TCT configuration which provides the best performances under most cases of PSC: uneven or random distribution of partial shading. However, for some cases of PSC this is not the case because the performance depends strongly on the patterns of shade. Therefore, this paper will enrich and complement previous studies in this area through a detailed analysis and comprehensive study of the different PV array configurations under all possible shading scenarios which may occur in reality, the thing that has not been carried out previously.In addition, these results represent an interesting guide which provides useful and relevant information concerning the performance of each PV array configuration under any pattern of shading. This will allow the estimation and prediction of maximum power and consequently will help to select the most suitable PV array configuration.

Voltage–Current Curves to Characterize Thermoelectric Generators

There are many ways to experimentally characterize thermoelectric generator (TEG) performance, but most methods provide an incomplete picture. The authors propose using voltage–current (V–I) curves generated at two different thermal conditions to provide an estimation of maximum power, optimum efficiency, ZT of the device, and thermal resistance due to ceramics and thermal interface materials on the outside of the thermoelectric material (HSR). The two thermal conditions are both steady state, electrically open in one case and electrically shorted in the other, and the heat flow into the device is adjusted to keep the hot-side and cold-side temperatures of the exterior of the module the same in both thermal conditions. The V–I curves are generated from four data points by instantaneously changing the external electrical load such that the TEG does not have time to respond thermally. After these two V–I curves are generated, the performance at any electrical condition can be predicted for the given hot-side and cold-side device temperatures. The authors present experimental data for a bismuth telluride (Bi2Te3) device as verification of this characterization method.